Research

Current and past projects include:

Computational design of peptide based binders for biomarker recognition

An ideal binder should be capable of capturing with high affinity, sensitivity, and specificity a target molecule, such as an organic molecule or a biomolecule and typical receptors for proteins are antibodies optimized in vivo. However, while monoclonal antibodies are highly successful in therapeutics and diagnostics, their production is costly, time consuming, is carried out in vivo, and does not allow for a control over the specificity of their binding site. Alternatives include minibodies also typically optimised in vivo, as well as DNAs and RNAs based aptamers evolved in vitro by the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) a combinatorial chemistry technique comprising of successive purifications and PCR amplification [1], in both cases the main shortcoming being the costly and time consuming procedure.

Exploiting advances in computing power together with the development of new and powerful algorithms for protein folding, docking, and structure prediction would ideally reproduce the same output, namely an optimized antibody, in silico. However, while progresses are being made in this direction [2,3], the design of a full antibody from scratches poses a set of challenges due to its structural complexity and it is still out of reach. Limitation not existent in the case of peptides, due to the smaller number of monomers they are composed of.

Computer optimized peptides have already been successful in the framework of drug recognition. In particular a recent algorithm based on replica exchange Monte Carlo proposed by Laio and co-workers, is able to optimize simultaneously the sequence and conformation of small peptides in order to reach a high binding affinity to a target organic molecule [4]. The same algorithm has been recently used to generate peptides for protein recognition [5]. Our results on the Maltose Binding Protein, the prototypical system for the study of protein-ligand recognition, have shown how computationally-generated peptides can bind with nM affinity a target protein. Recent algorithm developments allow generating peptides with an even higher affinity towards a target drug, and the new promising version of the algorithm is currently being implemented for protein recognition.

In addition, to push forward the binding affinity of a binder towards its target we further propose to build a molecular probe consisting of two peptides linked through a flexible spacer. Coupled peptides can exist in different forms [6,7]. For example, calixarene and porphirins as scaffolds are functionalized with peptides loops to mimic antibodies binding abilities, but these scaffolds are little versatile. Using instead a longer flexible linker will instead enable two binding moieties to reach two different sites. In this context, we have theoretically shown that flexible linkers are a viable option to enable two generic binding moieties to reach two different sites as the free energy cost of dissociating two coupled binders is higher than that of dissociating two single binders [8].

From a technological point of view, of particular interest are the patterned surfaces produced by 2-dimensional (2D) self-organised molecular layers as these systems have a wide range of properties such as luminescence, magnetism, and biological reactivity. For these properties they have potential applications as components for the fabrication of (molecular) transistors, miniaturised data storage devices, and sensors.

The two-dimensional patterns formed by molecules on surfaces can be modelled at different levels of approximation. While lattice models have been proven useful [1] for flat surfaces, or for surfaces whose role is only that of keeping the molecular structures planar as often happens with the Au(111) surface, more detailed models such as DFT calculations should be used to take into account the effect of the surface. In fact, more structured surfaces, such as Au(110) or those obtained with a former molecular coating, appear to affect strongly the self-organised molecular overlayer [2]. The molecular patterns can be controlled by the underlying surface, which in turn allows for a control of the electronic and magnetic properties of the combined system.

Coated nanoparticles have been objects of several studies, mainly concerning colloidal aggregates, thiol-coated particles, and Janus particles. We used Monte Carlo simulations to study the effect of the polidispersity of the coating, which is highly relevant when the nanoparticles size is as small as few nanometers. The effect of the polidispersity of coating on the pore size and reactant diffusion is still unknown and will have repercussions in the reactivity of core-shell nanoparticles for catalysis and in the design of a variety of systems.

Collaborators: S.Bon (University of Warwick) and D.Cheung (National University of Ireland Galway)

If we consider chemical systems, such as the crystal structure or the supramolecular assembly of an ensemble of molecules, a common question will arise: given N molecules, what is the lowest-energy organized structure that they can form?

This problem can be solved with the decomposition of the system in N agents. An agent is a system capable of exchanging information with other agents and its environment, taking decisions and performing autonomous actions.

The agent-based technique, previously used to study social phenomena, was first applied to a chemical system by Troisi et al. [1]. This technique allows a system of rigid shapes to evolve to the lowest-energy ordered structure on a 2-D lattice following a combination of stochastic, deterministic and adaptive rules with less computational effort than comparable Monte Carlo simulations. We have further developed the code to take into account realistic systems: a new set of rules has been developed for the prediction of the self assembled structure of a set of idealized shapes in the 3D space [2] and 2D molecular systems [3].

The performance of the algorithm have been assessed by comparison with a conventional Metropolis Monte Carlo algorithm, and this have been applied on a large set of representative models of molecules. For all the systems studied, the agent based method consistently finds a significantly lower energy minima than the Monte Carlo algorithm because the system evolution includes elements of adaptation (new configurations induce new types of moves) and learning (past successful choices are repeated).